IB 935 
18 H3 
espy 1 



IRST SERIES NO. 62 



JUNE 1, 1922 



UNIVERSITY OF IOWA 
STUDIES 



STUDIES IN CHILD WELFARE 



VOLUME II 



NUMBER 2 



DIFFERENTIAL FECUNDITY IN IOWA 



BY 



HORNELL NORRIS HaRT 




PUBLISHED BY THE UNIVERSITY, IOWA CITY 



Issued semi-monthly throughout the year. Entered at the post office at Iowa City, 
Iowa, as second class matter. Acceptance for mailing at special rates of postage 
provided for in section 1103, Act of October 3, 1917, authorized on July 3, 1918 



Monogrip* 



UNIVERSITY OF IOWA STUDIES 
IN CHILD WELFARE 

Professor Bird T. Baldwin, Ph. D., Editor 



FROM THE IOWA CHILD WELFARE RESEARCH STATION 

VOLUME II NUMBER 2 



DIFFERENTIAL FECUNDITY IN IOWA 

A STUDY IN PARTIAL CORRELATION 



BY 



HORNELL NORRIS HART, Ph. D. 



PUBLISHED EY THE UNIVERSITY, IOWA CITY 



V 3 



CONTEXTS 

Foreword 5 

I. The Problem Stated 7 

II. Technique 7 

1. Derivation of Indices 7 

Table 1. Indices of Fecundity and of Certain More or 
Less Correlated Social Conditions in Iowa Counties in 
1915 9 

2. Calculation of Zero Order Correlations 13 

3. Calculation of Partial Correlations 13 

4. Fecundity Regressions 14 

III. Interpretation of Fecundity Correlations and Regressions. .15 

5. Rural-urban Distribution and Fecundity 15 

Table 2. AD Correlations 15 

6. Fecundity and the Age Index G 17 

Table 3. Distribution of Iowa Counties According to 
Fecundity (A) and Age Index (G) 17 

Table 4. AG Correlations 17 

Table 5. Distribution of States According to Average 
Age of Women 21-44 (B) and Age Distribution Index 
(G) 19 

Chart 1. Age Distribution of Iowa Women in 1850 and 
1910 20 

7. Iowa's Declining Fecundity 23 

8. Home Ownership and Fecundity (r 4M ) 23 

Table 6. AM Correlations 24 

9. Educational Status and Fecundity 25 

Table 7. AJ Correlations 25 

High School Attendance 26 

Table 8. AQ Correlations 26 

Past Education Reported by Adults 26 

Table 9. AL Correlations 27 

10. Religious Indices 28 

Table 10. AN Correlations 28 

Proportion Catholic 28 

Proportion Protestant 28 

Table 11. AO Correlations 29 

Table 12. AP Correlations 29 

11. Fecundity of the Foreign-Born 29 

Table 13. Values of G for various nativity Groups ... .30 

3 



4 CONTENTS 

12. Fecundity and Proportion Married 31 

Table 14. AI Correlations 31 

13. General Interpretation of Fecundity Correlations 31 

14. Curvilinear Regressions 33 

IV. Conclusions 34 

15. Summary 35 

16. Recommendations 35 

V. References Cited 37 



FOREWORD 

The fundamental aim of the Child Welfare Research Station is 
to help the State to conserve and to develop every child to the 
maximum ability consistent with its native, endowment and special 
aptitudes. In order to do this it is necessary that a series of search- 
ing investigations be made from time to time into the various 
aspects of child life in Iowa. 

The problem of child welfare in Iowa is intimately bound up 
with the number and quality of children born into our Iowa homes. 
These two factors condition in a large measure the practical 
methods of child rearing. 

This second study by Dr. Hart throws definite light on the gen- 
eral questions : What types of individuals in our State are be- 
coming parents? Are more children born, proportionately, in the 
city than in the country? Are the larger families found among 
the native born, among the home owners, among those of average 
or superior school training? The answers to these questions have 
direct and significant bearings on the future citizenship of our 
State. 

Bird T. Baldwin. 

Office of the Director, 

Iowa Child Welfare Research Station, 

University of Iowa. 



DIFFERENTIAL FECUNDITY IN IOWA 

I. THE PROBLEM STATED 

The problem of the declining birth rate is particularly acute in 
Iowa. As is pointed out in the writer's study of Selective Migra- 
tion (9, p. 122), the number of children per 1,000 women of child- 
bearing age had decreased in 1915 to less than 40 per cent of 
what it was in 1840. While this decline in fecundity* is more 
spectacular than differential fecundity at the present date, the 
latter is of far greater consequence from a sociological point of 
view. 

The rural districts in Iowa have been much more fecund than 
the cities, and the foreign-born have had more children than the 
native-born, but no searching study of differential fecundity in 
Iowa has been made. (9, p. 123). The present inquiry seeks in- 
formation primarily as to what types of persons in the Iowa 
population are reproducing most rapidly, and as to the extent of 
the differences in their fecundity. 

II. TECHNIQUE 

It is proposed to attack the problem by means of linear partial 
regression equations predicting in terms of other correlated indices 
fedundity rates in the 99 counties of Iowa. 

1. Derivation of Indices. The latest, and by far the most com- 
plete, statistics relating to the characteristics of the populations of 
the counties of Iowa are contaiued in the 1915 State Census. (13). 
On the basis of these data indices have been derived with a view to 
testing, as far as possible, the current hypothesis relative to dif- 
ferential fecundity, such as that rural populations are more fecund 
than urban, foreign-born more fecund than native-born, the poor 
and ignorant more fecund than the well-to-do and the well educated, 
and Catholics more fecund than Protestants. The following indices 
were derived, and were designated by the letters indicated. 

A is the fecundity index, consisting in the number of children 
under five years of age per 1,000 women 21 to 44 years of age 
(13, pp. 418ff.). The age span employed differs from that used by 
Wilcox (37) because of the method of age classification employed 
by the state census. This fecundity index in more desirable for 



♦By the term "fecundity" as used in this study is meant not the physiological power of 
procreation but the characteristic of actually producing offspring. 

7 



8 IOWA STUDIES IN CHILD WELFARE 

the study in hand than birth-rates would have been, even had ac- 
curate birth-rates been available, because the number of children 
under five years of age already has had deducted from it most of 
the deaths of early infancy, and hence this index represents more 
nearly net fecundity than would birth rates. Being based on the 
number of women of child-bearing age, this index in far more 
useful than any rate per 1,000 of population, for the variations in 
the proportion of women of child-bearing age in the population 
introduce serious errors into calculations where crude birth-rates 
are employed. 

D is the percentage of the population living in cities and towns 
(State Census 13, pp. 606-7). This is not the percentage of urban 
population as defined by the United States Census, for the Iowa 
data include all incorporated places as urban, while the Federal 
Census excludes places under 2,500. 

6 is an index of the age distribution of women. It consists in 
the number of women 19 to 20 years of age per 1,000 women 45 
years of age and over. This index, as illustrated in Chart I on 
page 20, is -15*^-1 (13, pp. 418-33). The reason for choosing these 
age groups is that the age groupings reported by the State Census 
are as follows : zero to four years, five to nine, 10 to 17, 18 to 20, 
21 to 44, and 45 and over. The two groups selected are those 
lying next above and below the age of child-bearing (21 to 44). 

I is the number married per 1,000 females 21 to 44 (13, pp. 
491ff.). 

J is the number per 1,000 males 10 to 17 years of age attending 
school nine months or over in 1914 (13, pp. 418ff., 512ff.). 

L is the number, per 1,000 persons over school age, who were 
reported as having attended school eight years or more (13, pp. 
523ff.). 

M is the number owning homes per 1,000 persons 21 to 44 years 
of age (13, pp. 418ff., 618ff.). 

N is the number of persons reported as members of Catholic 
churches per 1,000 of population (13, pp. 418ff., 705ff.). 

O is the number of non-Catholic church members reported per 
1,000 of population (13, pp. 418ff., 705ff.). 

P is the number of foreign-born persons per 1,000 of population 
(13, p. xlix). 

Q is the number of persons attending high school in 1914 per 
1,000 persons 10 to 17 years of age in 1915 (13, pp. 418ff., 518ff.). 

These indices, for the 99 counties of Iowa, are shown in Table 1. 



DIFFERENTIAL FECUNDITY IN IOWA 



1-5 \& 



Ll-Ql suosaad 

OOO'I Jad ^ "^ — — ^ ~ ^ 



i- 
Z 

D 
O 
U 

< 

■S 

o 



[ooqos q3tq 
iiuipuauB suosaaj 



to^onoNoo 






uoiiBindodO O O O og CO co 

* j j O lO CO ^ r—t O OSi 

000*1 JadS , ^,-."3 

'IHHi-u.d.nn.j 



co as o oo co 

t-T}<O(N00 



mil iCjli.ft.Mi 

OOO'I «aB J3 qig££ggog 
-ujaiu qDjnqD ;C g CM CM CM CM CM 

3I[omBO-UON 



z £ 

Z co 

o 

a <° 

Z «, 

O r 



■< CO 

5g 

« *♦-' I 

i 2 

►J r 

O Ci 



uoi;B|ndod 

OOO'I J^d sjaq 
-mam ipjnqo 



000*1 -*»<* 
tjoujoq 3uiumo 
fr^-IS suo'saaj 



dJOUI 10 SJUd^ s 

^ papuane 3uiAeq 

SB p.il-io.l.i I 93B 
lOOipK J9AO SUOSJaj 



11-01 *»!*"* OOO'I 

jad sq^uoai 6 

looips auipuai 

-IB il-OI »[8W 



WCOHTfN 



CO CM CO i-H <N 



OOl CO "** LO © CO 

co r-iirs (Mas co 
cm 



CO OS Tf co CO 

t-i-l ^ CO 



(N CO UO CM i-H 
CO -^ ITS 03 CO 



CO CO 
O t- 



OS tr- »— I CO CO 
<N ■<* CO CM in 



•*J« CO CD rH i-H 
^ O ^ CO CO 
t> CO CO lO CO 



CCt-HtNO 
C- CO CO CO CO 



OOOHTfoO 

oo c- -«* as ic 

CO O C- LO CO 



t> t> 


O00H00 00 


t> to CO >-i CO 


COHHfH 


CO r-( 


00 OS O O) tr- 


cs c- t- rt tn 




T*< .-H 


CO CM CO "'I' CM 


Tt* CO TJ* Tf LO 


HO tJ* ^ to lO 



ac 



W-IS uatuoM 
flnn'T Jed tfe-TZ co «H t- co co cm oo 
usmoAi paujBjug. ^ £: c- co 00 ?■ 



O . 
H X 

§1 

Z ft 

< ~ 
t- CO 



c- c- o cm o 

COO C-OS ■* 
C- C- CO C- t- 



00 CO in •** CM 
CM CD in O CO 
t- t- L- C- t- 



a 



•13AO 

PUB SJB3jt!? H H « O C) CO 

ok »m'. isri oo in co co io oi •* 

S»- 000 I Jsa CM CM CM CM CM CO 

06-81 uscuOAi 



o 

fc, 
o 
a 

z 

< 

5 
z 



O Q 



saasjd 

I ajBJocljooui co -<f 
ui uoi^Bindod ** 
jo %'u90 ja<i 



§ 

u 
a 

u. 

u. 

o 

CO 

Ed 

O 
3 

z 



ea[Biusj OOO'I £J ^! 
jed sjBd^ g|J^ w 
japun uajpfiqo 



c 

3 
O 



c 

c.2 

cu cd 

^ — i 

c^ 
c a 

■m c 
'u a 



CCWOTTH 
lO 00 O^ CO •*? 
CM CM CM CM CM 



COOOOSoi 
OS t- i-H CM IC 
CM CM CO CO CM 



00 00 ITS lO CM 
T* t> 1/3 'Cf T)l 



tj co in oo as 

T CO •fl' Tf ■» 



CO oo oo 
co tH co cm n- 

CD CO CO t^ t- 



o oo otr- co 

HCOXH51 

co -^ m co in 



oo CM oo Tj< CO 

t> oo rn m oo 
in co co co in 



cu V 

S o c 

m « 22 

•OT33 P. 3 



a 



c 

oi 2 a 
- co o v o 

CQaq wkcq 



(3 

S a § 5 



tZ 01 o o 

o; -tf S3 t. co 
3 3 a a a 
MMOOO 



MHO 

■O"* o 

CM CM CM 



00 CO CO 
■<* CO o 
00 t> t> 



IOHM 

e- in oo 
t)i in 1 * 



t- 00 tH 

in r)i cm 
t- c- t> 



O h-i o 
■* CO --H 
CM COCO 



O th 00 
C- CO t- 

m in in 



o 
-a 

O Ol 
m O 9 

0> o>^ 

ouu 



10 



IOWA STUDIES IN CHILD WELFARE 



i 'on 

P* .ICO 



00HHOJC) OINffiOOl MHCOt-^t t-OOt-MO C~ tH co o o 
CONOOIOO 00 "<* in in ■** NHNIOO) © CO CO "** © CO 00 in t- CO 



.0 05 
pL, ©iH 



MNOOWIO CO © t^ ■** © 
CM © © t- CO rHHMNOl 



© oo in oo co 
CO co © © m 



o © c- ©© 

CO © © ^ CO 



M* 00 © © 00 
© *-4 t> © r-i 



en © 
© o 

CO CO 



in © <M ^H -** 

Hl-tOlMOO 

coi-ii-i co co 



CO CO© COCO 

HMO<OtO 

co ^r co co >h 



CO CO © rt 00 
w CO CO CO CO 



"* © CO 00 © 

en T* io-*c © 

CO CO CO CO CO 



»H CO iffl C-© 
© CO C- 00 © 
CO CO COH 



■* CO 
I- CO 
CO 



Ot-MHt- 
■>* © cc* 



© ©co tji in 

CO© c- 



HHt-HCO 
CO CO r-H in rH 



C»>-l CO ^ CO 
TC 00 CO t- CO 



© *^ © in oo 

©CO CO CO © 
CO CO 



© t- 
© t~ 



CO CO Tf -^ CO 

oo © in © "^ 



00© t- 00 CO 
00 -* >* CO ■<* 



eo in© ©co 

co co © in co 



m oo 
■* © 



-H t- CO coco 

t- Tf © CO .-I 

t- © t- c- t> 



© in th co i— i 

© t- © H Tf 

c~ © © © t— 



© © in in oo 

t- CO 00 © i-H 
© t> © © F- 



© co in © i-h 

CO O 00 © ^ 

© t> t- t> © 



© "*f CO *# *r 
© c- © C- © 



cot- 

00 © 
■* CO 



© r-l CO © © 
•^ 00 CO CO © 

tjh co © in m 



r-( © ^h © in 
© tH o © co 
,_, co in© «* 



i— i co *-< in i-H 

00 00 CO © © 

in -^ in in co 



in <-h © ©co 
t— i © m i- 1 in 
t- co co co co 



co co co © o 
i-H co m t> rji 
co m m tj< tj* 



©© 


© CO t- © 00 


CO © 


-* co © -* c- 


c- c- 


C- L- © t- t- 



© © © c- © 
© t- co in oo 

oo c- t- © t- 



■* in o i-ico 

00 © © ■* i-l 
lOC-f-t-00 



© i-i rji in t- 
© m © oo co 
c- © c- t- c- 



c- coin ■«* © 

t- Tf" 00 .-I CO 
t- t- E- t- t- 



©o 

Ooo © 



CO CO CO >H CO 

co c- c- ** m 

COCO CO CO CO 



CO © CO© t 
CO © Tf CO © 

co cq co co co 



co © oo in t-h 

Ht-coiOM 
CO CO CO CO CO 



co oo © m © 
co ao co © © 

CO CO CO CO CO 



■-< © n< © —I 
^mnoo 

CO CO COCO CO 



00 c- 
Q CO CO 



© © © in © 
c- in m m co 



CO o 
© © 



CO t- CO CO rH 00 CO CO © "# 
CO ^H © ** rH WH00H1O 

© © rf © © ©t--©-^i© 



© © co t-h m 
in t- © in © 



CO t> t> t-h © 00 C- © CO CO 

© oo co in in © © © © co 
© © t> © © © in © in © 



in 



B 
OO 



a a 



§§-2i* 

ooouO 



■r. 


P 
+J 


£s 




> 


o 


.2 m 


X 


d 


OJ 


QJ OJ 




QQQQQ 



3 S M, — i fn 



E o> Sj'c 

hhh3» 

feOOCJffi 



c 



o.S 
c - 



c w 



tO K- ** 

a s 

« a; o 



ffi ffi Ph ffi ffi 



DIFFERENTIAL FECUNDITY IN IOWA 



11 



■8 

3 
C 

o 
U 

lH 

H 

M 
< 


nt-OTfffl 
Cy i-< oo © CO in 

CM r-l i-t rH i-l 

1 


CO ■* CM CO CO 

OS in 00 00 CO 
l-< CM r-t 1-1 1-H 


t- in os t- cm 

00 i-H CM ■* CM 

iH CM CM i-l iH 


^J* OS t— C— OS 

^OlHrtlO 
1-t w i-t CM i-t 


©C0OC0-"* 

co© os co m 

CMi-l MH 


© i* o 

00 i-l 00 

i-t r-1 r* 


loco t- oo 

CL| <0 t> O O Oi 

IHHHH 


oo cm in ■* as 

Tf OS O ^ CO 
i-H i-t 


CO T-H Tj* CO CO 

co co co m t-i 

i-l CM 


t-m cm os oo 

CMC- OS 00 -^ 


cm i-t in os in 

CM O CM O O 

i-t i-1 i-t tH tH 


■** i-H r* 
© © © 
tH iH 


o 


t-t-«OH 

CM i-l CO •* CO 


o© ©o c- 

OS CO CM CM CO 
CM t-h CM CO t-H 


t- •* tC 00 CO 

o in oo co m 

CM CM CM CM i-H 


oo 00 OS Tj< 

•** in in © © 

CO CM CO CM CM 


i-< co co in © 

MrtlOrHt- 
COCM i-H COCM 


©COOS 
© CO CO 
CM CM CO 


z 


i-H t- t- © Tf 
■*•<*© CM IN 


OHt-CJiO 

co in os oo m 

i-H i-l 


tr- os o co in 

t> CD CM CO 

I-t 


© © CO OS CO 


tH O 00 CM OS 

co co in i-i in 

I-I 


00 O CM 

CO CO t-t 


§ 


HHNHOl 

co CM m c- co 


NOOOOSrt 
TflOlOCCCO 


CO OS CO CM O 

o cm m co cm 


lOt-HClH 
00 CM *» CM -tf 


OS ■* CO © ■* 

in co i-» co i-t 


iJOW 
CM .-I CM 




J 


CM CO CO CM "5 

oo © cm in as 

© C- 00 CO CO 


CM-* COOS >-l 
H03 10COC- 
00 00 C- C~ CO 


00 00 CM ©CO 
CO i-l OS cs ■*)< 
C- C- © C- © 


o oo in oo o 

HTflOOS'* 

oo © © © © 


OS CS OS © © 

cc*^iiniir 
© © ■* t> t- 


■n- oc in 

i-t ©CO 

oo © m 


1-3 


CO in CO OS OS 
C- O OS CS in 

^ji in co co m 


oo © t- © in 

00 rH CM ^ O 
CO CO ■<* •** ■* 


i-t 00 © OS CO 

os oo cm in oo 
■* cd in co co 


in in i-i o cm 
oo in co © oo 
CM -3t TP © in 


■* tr- © C- e- 

tDt-HMO 

co •<* co in © 


CM 00 T 
O Tj< CM 

© CO © 


K- 1 


CO CO COO CO 

co'tinoco 

C- C- C- C- t> 


C- CO CO CO t> 
CO t- C- CO 00 

t> CO c- c- c~ 


iniOHt^o 
c- T-i t- oo in 

© C- C- C- [- 


LCHOC-O 
00 ■* 00 CO CM 
o c- t> t- t- 


CM © 00 CS C- 
O © © "» CM 
C- 00 00 L- C- 


C-lOO 

co ■* in 
c- c- t> 


o 


Tf CO in CO o 

^h in oo ^ m 

CO CO CM CM CM 


COO^OOO 

■* c- in ■* as 

CM CM CM CM CO 


00 ■"* CO ■* CM 
i-H © CO (M CM 
CM CM CM CM •* 


oo co in •* i-t 
i-* in co co cm 

CM CM CM CM CO 


o in o in os 

©CM CO ^ CO 
CM CO COCM CM 


© ow 

M1t« 


Q 


tji o o co-"* 

■* Tl< CO Tf Tj< 


00 CO CM CM m 

-** in ^f -* co 


o in © © oo 

C- C- Tl"* CO 


CO 00 CO CM C- 
CO ■*!<"*© -tf 


© rf Tt< in os 
■* cm cm in © 


os © in 

fojio 


<! 


t> CO tH Tjt in 
in CM CO in i-t 

CO CO CO 1/5 CO 


in oo co co co 
in o os oo c- 

IDlOlOlflt- 


t-h © CM CO CM 

in ■* in © i— < 

Tf Tjt © © CO 


i-h © in in © 

CM CO ^ CM O 

© in © m © 


i-i CM t- © OS 

00 CM CO © CO 

w c- c- in ■>* 


OS 00 © 
CO Tji t- 

© t> in 


3 
o 


Humboldt 

Ida 

Iowa 

Jackson 

Jasper 


Jefferson 

Johnson 

Jones 

Keokuk 

Kossuth 


Lee 
Linn 
Louisa 
Lucas 

Lyon 


Madison 

Mahaska 

Marion 

Marshall 

Mills 


Mitchell 

Monona 

Monroe 

Montgomery 

Muscatine 


O'Brien 
Osceola 
Page 



12 



IOWA STUDIES IN CHILD WELFARE 





0> 


OS O 
i-H in 
rH rH 


coin oo in os 

OCOlOOOfO 
NNHNH 


Nt-tOincO 
OS TH tH CO in 
rH rH r-l rH CM 


CO OS OS OS OS 
C- CD 00 OS 00 
i-t r-4 r^t i~^ r~t 


t- m jo co oo 

rH t- 00 COCO 
CO CM rH rH rH 


OSHt-H 

OSrH NO 

CM rHCM 




Oh 


OS CO 

CM in 


CD OS CO tH CM 
NON[-M 
rH r-t i-H 


OSrH OS CO t- 
Ht-tDtOO 
rH rH i-H CM rH 


CO CO CO OS CO 
CO CM in rH CO 
rH 


■H< -r)< CO CO CM 
CM ■* CM in 00 

rH rH 


in oo os in 
■** c-mco 

rH rH i-H t~t 


o 


mo 
c-co 

t-H r-t 


(OCJOIHO 
CM CM rH CO CO 


rH OJ 00 IOCS 
^■OC-CllO 
CM rH CM CM CO 


CM CO OS rH O 
rH Tf 00 CD CO 
CM CO CM CM OJ 


t— oo os oo in 

in t- CO rH rH 

COCO CO CM CO 


co n* o CD 
CO CD CM a 1 
CM rH CO CM 




z 


O t> 

rH OS 

CMrH 


OOMCOOt 
i-H OS C~ CO tt 
CM 


OS O OS rH rH 
T CO rH rH T* 
CM rH 


OOHHlOt- 
00 CM l> CO 


in oo o r- co 

00 C- rH CO 
rH 


m o cm t- 

os oo cm in 

rH 




3 


CO o 


00 00 to CO CO 


O © CM CO CD 

CM O CO rH CM 


CM CM CO CD lO 

loc-coon 


oo t- m Tt os 
co in t- co in 


TP in in rH 

t> oo -m cm 


T3 

<U 

3 
C 
'-£ 

c 
o 
U 

1-1 

w 

cq 
< 

E-i 


J 


—i cm 

•* o 


00 t- m CO 00 
CO CO CO C~ t~ 


O U0 rH U0 OS 
O CO t> O OS 
L— I— in CO CD 


CM CO CM 00 Tf 
O OS CO 00 CO 
t- CO C- t- CO 


t- rH o o •* 

rH OS CO CD Tf 
00 C- t- CO CO 


O O CM CO 

CM CO OS OS 

co in th c~ 


1-3 


00 C- 

00 -* 
co in 


00 O —1 00 rH 
CD in -rj* CM CO 
T* C- CO lO CO 


CM lO CO CO 00 
CO — < m CM 00 
TJ< t— rj« co ^ 


CM Tr C- CO CM 
OS O O CM t- 

co t* in co in 


rH co oo in ** 
Tf m co in co 


tooooo 
O O CO o 
co t- cm m 


'o •>* 

H >OH 

M t-c- 


CD IN CM 00 fr- 

TfOlOlOlO 

t- c- t- c-oo 


00 CO OS *-* to 

U0 OS t •* rH 
t- CO t- L— t> 


OS CO OS t- CD 
t- 00 TT C- tii 

c- c- c-c-c- 


os in t- co oo 

tlOOOCNO 

t- c- 1- t- c- 


t-OtDH 

in o co cd 
CO t- 1- 1- 


NOO 
,03 M 


oooont- 

COCO CO CM CM 


rH OS CO [- CM 
OlOISOt- 
CO CM C0-* CM 


os in os os in 
in co o t- co 

CM CM CM rH CM 


os co in t- o 

CO CM TP t- CD 
CM CM CM CO CO 


CM O CM 00 

CO OS l-H rH 
CM CO CO CO 


Q 


O rH 


•* C- 00 t- CO 

CO 00 CO Tf CO 


CO rH T« O00 
-cf 00 CO Tj» in 


CO OS OS CO o 

•"ji co in co c- 


coco in co cm 

CO Tt< TJ" CO TC 


CM CO COCO 

co oo com 


<! 


CDrH 
CM l> 

t- to 


OOHNH 
O CO CO CD t- 
L— T* in in CD 


■*l>HHO 
-rj< rH rH 00 t- 
CD TP t> t> m 


O00 00 ooo 

O t- O CD rH 

co m in in in 


CO tr- OS O OS 

O(0t-00 

cd in in cd t- 


00 OS i> 1- 

O t- T«Tf< 
CO T* CD CO 


C 
3 


O 


■31 

Cm On 


Pocahontas 

Polk 

Pottawattamie 

Poweshiek 

Ringgold 


Sac 

Scott 

Shelby 

Sioux 

Story 


Tama 
Taylor 
Union 
Van Buren 
Wapello 


Warren 

Washington 

Wayne 

Webster 

Winnebago 


Winneshiek 
Woodbury 
Worth 
Wright 



DIFFERENTIAL FECUNDITY IN IOWA 13 

2. Calculation of Zero Order Correlations. All of the linear 
correlations in this study were calculated without grouping of items. 
The method first used was to assume as the average for each 
index, thus avoiding the use of negative deviations and products. 
In practice, however, this method proved to require great labor 
because of the large size of the squares and products involved. In 
order to utilize the zero order correlations for the calculation of 
partials of higher orders great accuracy is essential. This has been 
insured by calculating all of the zero order correlations twice, 
using two different assumed averages, and requiring that tb.3 results 
check within .00005. All correlations were carried out to five 
places. It was recognized, of course, that the probable errors of 
the coefficients, due to random sampling, were so large as to make 
five place r's ridiculous for the purposes of interpretation; this 
degree of accuracy is required merely because of algebraic reasons. 
Correlations are given in this text to only two places. 

3. Calculation of Partial Correlations. To carry the calcula- 
tion of seventh order partial correlations to the degree of accuracy 
required would prove an immense task if all the values of \/l-v" 
had to be calculated by ordinary methods. To obviate this diffi- 
culty the author has devised a compact chart from which the 
required values may be read off directly within an error of .00005. 
Seventh order partials were derived by these methods with maxi- 
mum errors of .0001. Most of the calculations were performed, 
under the writer's direction, by Mr. James Sarkisian and Mr. 
Arnold Wilbur. 

Professor H. L. Rietz, who has been kind enough to read and 
criticize this paper, raises the question whether the formula for 
the probable errors of partial correlations is valid when used with 
only 99 cases. It is obvious, of course, that if the number of items 
considered were only as large as the number of variables involved 
(e. g. two items for zero order correlations, three items for first 
order, four for second order, and so on ) perfect positive or negative 
correlations would always result, no matter what the true relation- 
ship between the variables. In such a case, obviously, the usual 
formula for probable error would be highly erroneous. This same 
type of error will be present in diminished degree unless a con- 
siderable number of items is involved. In defense of the use of 
partials in this study it may be urged, first that the seventh order 
partials show no signs of approaching ±1.0; that the partials 



14 IOWA STUDIES IN CHILD WELFARE 

appear to act consistently when new variables are added, as will 
be seen from a study of the tables which follow in the text; that 
the final conclusions are based chiefly on correlations of less than 
the fourth order ; and that the results as a whole are self-consistent, 
and consistent with the results of other studies. 

In passing it may be well to refer to the methods developed by 
Truman Kelly for calculating partials (15, 16). His original tables 
are, as he himself recognizes (15, pp. 5 and 6), not carried out for 
enough places to be of use in calculating partials beyond the first 
or second order. His more recent chart does not permit of suf- 
ficient accuracy for the purposes of the present study. His method 
of successixe approximation was not attempted. Where it is 
important to be able to tudy the effect upon the original correlation 
of assigning one factor after another it is a great advantage to 
have the series worked out step by step. It is not necessary, of 
course, to calculate the partial correlations of all of the possible 
combinations of the indices. 

The first series of partials calculated involved only the indices 
A, D, I, J, L, M, N, 0, and P. When the results were analyzed, 
certain conditions appeared to suggest the presence of an uncon- 
trolled age variable, and index G was developed. The failure to 
establish any important correlations between A and J when D 
was constant seemed so surprising that index Q was developed. A 
study of the data made it seem unnecessary to go farther than the 
fourth order with partials involving G and Q. 

■i. Fecundity Regressions. For several purposes regression 
coefficients are more useful than correlation coefficients. The 
regressions recpiired have been calculated by the methods outlined 
by Yule (41, p. 240). In calculating the probable errors of re- 
gression coefficients the writer has expressed the formula given 
by Yule (41, p. 253), in the more convenient form: 



,,„, .6745 b„ K Vl-r-, „ K 

P.E.b 12 .K=— = -***- -^ 
Vn r.„ t . 



Where K represents any collection of secondary subscripts other 
than 1 or 2. 

The advantage of this formula over the one given by Yule is that 
the latter involves the quantity <r 2 . K , which must ordinarily be 



DIFFERENTIAL FECUNDITY IN IOWA 



15 



specially calculated for this use, whereas the substitute formula 
involves only quantities which have already been calculated. 

In interpreting correlations in this study, interest centers about 
differences, not between county and county, but between various 
types of people. Counties are used simply as convenient units for 
investigation. It happens that certain very significant factors, 
such as home ownership, vary but little from county to county. 
Correlations of fecundity with these indices, by counties, therefore 
are low. At the well-recognized risk of misinterpreting trends 
beyond the range of observation, the predictions of fecundity in 
counties where these indices would reach their theoretical extremes 
have been calculated, with a view to predicting for comparison the 
fecundity of a population wholly of a given type with the fecundity 
of a population wholly of the opposite type. 



III. INTERPRETATION OF FECUNDITY CORRELATIONS 
AND REGRESSIONS 

5. Rural-Urban Distribution and Fecundity. The highest cor- 
relations which occur in the entire series are those between fecundity 
and percentage of urban population. The partial correlations 
derived between A and D with various other factors assigned, are 
shown in Table 2. 







TABLE 2 










AD Correlations 






r AD 


— .74±.03 


r ADILNOP — .73±.03 


r AD.LMNOP 


— .77±.03 


r ADG 


— .84±.02 


r AD.ILMNOP -72 — .03 


r AD.LMNOP 


— .77±.03 


r AD GI 


— .79±.03 


r AD IMNOP — .71±.03 


r AD.LMOP 


— .77±.03 


r AD.GIM 


— .73±.03 


Tad.ipq -.70±.03 


r AD.LNOP 


— .81±.02 


r AD.GIP 


— .79±.03 


i-ad.j -.62±.04 


r AD.MN 


— .77±.03 


r AD.GIQ 


-~.77±.03 


1-adjlmn- -67±.04 


r AD.MNOP 


— .77±.03 


r AD GM 


— .76±.03 


r — Gl-*- 04 
'ad.jlmno ■"- ■"* 


r AD.MOP 


— .77±.03 


r AD 1 


— .67±.04 


'adjlmnop ■"'' — "^ 


r AD.NOP 


— .81±.02 


r AD.IJLMNO 


—.60 ±.04 


r. U) , LMNP -.67+.04 


r AD.O 


— .76±.03 


F AD IJLMNOP 


— .62±.04 


r AD.JLMOP — .69±.04 


r AD.OP 


— .80±.02 


r AD IJLMNP 


— .59±.04 


r — fi9-*- 04 

r AD JLNOP •°° u * 


r AD.P 


— .80±.02 


r AD.IJLMOP 


— .64±.04 


Tad.jmnop --68±.04 


r AD.PQ 


— .77±.03 


r AD IJLNOP 


— .60±.04 


Tad.lmn -.78±.03 


r AD.Q 


— .72±.03 



r ADG = — .84±.02*; i. e. the correlation between fecundity and 



♦In interpreting this and other probable errors in this table and the following tables, 
the fact that only 99 items are involved should be borne in mind. The probable errors 
for the higher order partials are not very reliable. 



16 IOWA STUDIES IN CHILD WELFARE 

percentage of urban population when the age index is assigned is 
as likely as not to be between — -.82 and — .86, while the chances are 
1,000 to 1 that the true correlation lies between — .76 and —.92. 

This correlation is due in part to the negative correlation between 
percentage of population living in cities and percentage of women 
married : r D i= — -48 ± .05. 

Certain of the other variables account to a small extent for t'x 
correlation, but the lowest value reached is — .59 ±.05, so that a 
clearly established negative correlation exists between fecundity 
and the percentage of urban population, even when the other indies 
listed are assigned. 

In general, three types of explanation must be considered for a 
correlation r X v. First, the presence or size of X may result from 
the presence or size of Y; second, the presence or size of Y may 
result from the presence or size of X; third, both X and Y may 
result from some common cause or group of causes. In the ease of 
r AD , it might theoretically be true that families move to the country, 
or stay in the country, because they have babies, that families in the 
country tend to have more babies than families in the city because 
rural conditions are more favorable to fecundity than urban con- 
ditions are, or that some common element, such as contentment with 
a vegetative, domestic life, favors both remaining in the country 
and rearing children. Of these three possibilities, the first seems 
least likely, and the second most likely, though final de- 
cision must wait upon further inquiry. 

In any case it seems safe to conclude that women in cities have 
fewer children than women in rural districts in Iowa, apart from 
differences correlated with age distribution, nativity, religion, edu- 
cation and home ownership. The amount of this difference in 
fecundity is suggested by the regression equation for predicting A 
from D, which is : 

A' D =612±5— (4.4±.3)(L— 46±1)±37.* 

If the percentage of urban population were zero, the fecundity 
rate, according to this equation, would be 814±51 ; if the percentage 
of urban population were 100, the fecundity would be 374±41. 
The difference between these two rates, or — 440 ±65, represents 
the maximum change in A which would correspond with a change 



♦In this and subsequent regression equations the probable errors of the averages and 
regression coefficients are stated after the respective numbers, with a ± sign. The last 
number in each regression equation is the probable error of estimate. 



DIFFERENTIAL FECUNDITY IN IOWA 



17 



of the whole possible range of D. As a check on this reasoning it 
should be noted that the fecundity rates as estimated from data 
dealt with in the writer's study of migration (9, p. 124) are 416 
for purely rural territory, and 755 for urban territory. The dif- 
ference between these two latter rates is — 339, instead of — 440 
as estimated by regressions. The difference between the two esti- 
mates (which is less than twice the probable error of the regression 
prediction) is doubtless due, as will be shown later, to the existence 
of a curvilinear instead of rectilinear relationship between fecundity 
and rural-urban distribution. 

6. Fecundity and the Age Index G. The relationship between 
A and G is shown in Table 3. 



TABLE 3 

Distribution of Iowa Counties According to Fecundity (A) and 

Age Index (G) 

Derived from Table 1. 




Fecundity 


Age 
Index 


400- 
449 


450- 
499 



2 

2 



1 



5 


500- 
549 



4 

3 

2 





9 


550- 
599 

1 

9 

9 

2 




21 


600- 
649 



4 
13 

8 

3 


28 


650- 
699 



3 

7 
5 
2 


17 


700- 
749 





2 

7 

2 

1 
12 


750- 
799 









1 

1 

2 


800- 
845 











1 
1 


Total 

1 
23 
38 
26 

9 

3 
99 


150-199 
200-249 
250-299 
300-349 
350-309 
400-499 
Total 



1 
2 
1 


4 



Apart from the AD correlations the highest correlation with A 
found in the study is r A a.Di=+.75±.03. The AG correlations are 
shown in Table 4. 





TABLE 4 






AG Correlations 




r AG 


+53±.05 


r A G.Dip +62±.04 


r AG L 


+ 51±.05 


r AG.D 


+.73±.03 


r A G.DiQ +-73±.03 


r AC.M 


+.69±.04 


r AG.DI 


+.75±.03 


,-,,.,,„, +68±.04 


r AG.N 


+.54±.05 


r AG DIJ 


+75±.06 


+61±.04 


1- AG.O 


+.54±.05 


r AG.DIL 


-f.72±.03 


i'ac ip +.51 ±.05 


r AG P 


+.55±.05 


r AG.DIM 


+.69 ±.04 


r AGJ +66±.06 


r \c; Q 


+.47±.05 



The lowest value in this series is r AG .Q=+.47±.05. It seems safe 
to conclude, therefore, that the age distribution of the women, 
when other factors noted are assigned, has a high, clearly estab- 



18 IOWA STUDIES IN CHILD WELFARE 

lished, positive correlation with fecundity. The interpretation of 
correlations involving G is not, however, as simple as it at first 
seems. The correlation r A c=+-53±.05 might mean that the type 
of age distribution where G is high results in a high net birth rate 
per 1,000 women of child-bearing age, or it might mean that a high 
fecundity tends to produce the type of age distribution where G 
is large, or it might mean that certain factors favorable to the type 
of age distribution where G is high are also favorable to high 
fecundity. 

With regard to the first of these possibilities, it has been recog- 
nized by other investigators that fecundity varies sharply with 
age. Whipple (36) quotes data from Budapest as indicating that 
fecundity for females reaches its maximum between the 18th and 
19th years, falling steadily to the age 50, when it practically ceases. 
Perhaps the best American data on births in relation to the age 
of the mothers are contained in the studies of Infant Mortality 
made in certain cities by the United States Children's Bureau (32). 
The United States Census gives the numbers of married women by 
broad age groups in these cities in 1910 (29). From these data 
rough estimates have been made as to the relative birth rates at 
various ages, for Brockton, Mass., Johnstown, Pa., Manchester, 
N. H., and Waterbury, Conn. Young (38) also presents data for 
New Hampshire. Better data are available for other countries. 
Webb's Dictionary of Statistics gives the mean annual number of 
births or accouchements per 1,000 wives by five year age groups, 
for Denmark, Sweden, Finland, Austria, France, New South Wales, 
Victoria and Western Australia, for various dates from 1871 to 
1903 (34). 

All of the above data support the conclusions noted by Whipple, 
that fecundity declines steadily from the teens until it disappears 
at about the age of 50. The present study has been concerned with 
the number of children, not per 1,000 married women, but per 
1,000 women of child-bearing age. Careful computations indicate 
that the above difference in birth rates would probably result in 
a difference in the fecundity indices between the women 25-29 
years of age and the group 40-44 of 350 to 500 points, depending 
upon which birth rates are assumed. Hence it will be seen that 
if the women 21 to 44 years of age were concentrated at the upper 
end of this age period their fecundity would be expected, other 
things being normal, to be approximately 240 points lower than 



DIFFERENTIAL FECUNDITY IN IOWA 



19 



the average, whereas if the women were concentrated at the ages of 
25 to 29 the expected fecundity would be about 160 above the 
average. 

In order more accurately to judge the significance of differences 
in average age as reflected in the G index, the value of G for each 
state in the United States in 1910 was calculated from United States 
Census data, (29, pp. 361ff., 548ff.) and for the 32 states in which 
G fell within the range of that variable in Iowa counties in 1915 
(which was 179 to 422) the weighted average age of the women 
21 to 44 was calculated, by five year periods. This average will 
hereafter be referred to as B. 

The relationship between average age (B) and the age distribu- 
tion index (G) is shown in Table 5. 



TABLE 5 
Distribution According to Average Age (B) and Age Distribu- 
tion (G) of Women 21-44 of the 32 States in which 
G lies between 179 AND 422 


Age Index 
(G) 


Average Ag 


e (B) 


31.00- 
31.24 


31.25- 
31.49 


31.50- 
31.74 


31.75- 
31.99 


32.00- 
32.24 


32.25- | 
32.49 | Total 


179-199 
200-249 
250-299 
300-349 
350-399 
400-422 







1 







3 
3 





2 
1 

3 " 





3 
6 
1 

10 




7 
2 


9 


3 






3 


3 



10 

10 

5 

4 

32 


Total 


1 


6 



The correlation r B G = — .985±.009. The regression equation 
of B on G is : 

B = 31.80 ±.01— (.0046 ±.0004) (G— 325±3) ±.07* 

With such an exceedingly close correlation it seems safe to assume 
that the average ages of women 21 to 44 in Iowa counties may be 
derived from G in terms of the above regression formula. The 
range in average age of women 21-44 years of age in Iowa Counties, 
as thus calculated from the actual range of G in Iowa counties is 
only 1.12 ±.07 years, which would have a negligible effect upon 
fecundity. Hence the correlations r A o do not indicate that the birth 
rate is an effect of the age distribution. 

The second possible interpretation of the AG correlations is that 



•See footnote on page 16 



20 



IOWA STUDIES IN CHILD WELFARE 



a high birth rate tends to produce the type of age distribution 
having a high value for G. It will be recalled that 



Gx = 1,000 



number of women 18-20 1,000 e 
number of women 45-)- f 



where x represents the year for which G is calculated. (See 
Chart 1). In a community with no immigration or emigration, 
the women 18 to 20 at date x would have been 1 to 3 years old at 
a date 17 years previous to x, and the women 45 and over at date 

130 

CHART 1 

Age Distribution of Iowa Women 

per 1000 women 21 to 44 years of age 

1850 and 1910 

Distributions are based on United States Census 

data (29) and (30). The age classification 

shown by the vertical lines is, however, 

that of the State Census (13). 




O 5 10 18 81 45 90 

x would have been 28 to 75, or thereabouts. If, in the x — 17, the 
fecundity of women 21 to 44 yere high, G would tend to be high 
in the year x, unless other factors, such as peculiarities in death 
rates, interferred. In other words, G x tends to be correlated with 
A ( . 17) , and if a correlation exists between A, X - 17 , and A x , 
a correlation may be expected between Gx and A x . Extensive 
migration might, however, tend to break up this correlation. 

Other factors besides the birth rate in preceding years, however, 
affect G. A high death rate among women over 20 years of age 
would tend to produce a high value of G. Now a high death rate 
of women over 20 might result from a high birth rate. It could 



DIFFERENTIAL FECUNDITY IN IOWA 21 

scarcely be a cause of a high birth rate. Under the first alternative 
deaths due to diseases caused by pregnancy and confinement come 
to mind. The Children's Bureau has shown that such deaths of 
mothers number approximately seven per 1,000 live births. A dif- 
ference of several hundred in the fecundity rate, therefore, could 
account for only a very small variation in G as a result of deaths 
due directly to child birth. Bearing and caring for excessive 
numbers of children might, however, lower the resistance of 
mothers and hence promote high death rates from other causes. 
Probably of greater importance is the well known correlation be- 
tween poverty and high death rates from tuberculosis and other 
causes. 

On this last point data collected by Dublin for the Metropolitan 
Life Insurance Company are significant (5). He compares death 
rates among Metropolitan Industrial policy holders, and persons of 
the same ages, sexes and races in the United States Registration 
Area. As to differences between these policy holders and the 
general population he indicates that the industrial policy holders 
represent a group definitely below the average of the country in 
economic status, but typical of the industrial population rather 
than typical of the poverty-stricken. Women in this group 20 
to 74 years of age show mortality rates, by five year age groups, 
from six to 29 per cent higher than women in the registration area 
of the United States. (5, p. 23). In a stationary population the 
mortality rates shown would result in an age distribution, among 
the women of industrial policy holding families, represented by 
6=151. For the Registration Area the corresponding index is 
G=142. This difference would, of course, be much more marked 
if the comparison were made between wage earners and persons 
of the professional and capitalistic classes than when made between 
wage earners and the general population, and would be still higher 
if a comparison of distinctly rich and poor classes were made. 

For a more extreme comparison between the adult female age 
distributions of poor and average well-to-do groups, the contrast 
between the colored and white populations may be cited. Dublin 
shows that colored females of the Metropolitan families had, in the 
age group 20 to 24, a mortality 238.5 per cent as large as that of 
white women of the same age, and that while this difference de- 
creased steadily in each successive age group, its lowest point was 



22 IOWA STUDIES IN CHILD WELFARE 

105.1 per cent of the white rate (5, p. 18). The effect of this 
difference in adult female death rates upon the G index may be 
determined directly from the United States Census. The following 
values of G and A are derived from the 1910 census (29, pp. 307, 
310, 356, 542). 

G A 

All white females in the United States, 1910 323 644 

All negro females in the United States, 1910 533 718 

All negro females in South Atlantic Division, 1910 549 815 

All Indian females in the United States, 1910 373 1092 

The total negro population of the United States is not affected 
appreciably by immigration and emigration, as I have shown else- 
where, (9, pp. 29-30), though the white population is. The high 
value of the G index for negroes is not due to extremely high net 
fecundity, judging by the 1910 and 1900 rates. The negro fecun- 
dity rate in 1900 was 822, according to the United States Census 
(29, p. 319). 

Clearly, the difference between the values of G for white and 
colored women must be the result chiefly of the difference in death 
rates, and while some may urge that this is the effect of racial dif- 
ferences, a more plausible hypothesis is that it results from the 
notoriously unfavorable economic sanitary and social conditions 
under which negroes live. 

The above evidence shows that a high value of G tends to indicate 
unfavorable economic and industrial conditions, resulting in a high 
adult female death rate and hence that the high AG correlations 
may be explained in considerable part in terms of correlation 
between poverty and a high birth rate. This conclusion is in 
harmony with Heron's findings (10). 

As another possibility it should be noted that the migration rate 
(as is indicated by analysis of data contained in (9) is probably 
much higher at the ages 18 to 20 than at the ages 45 and over: 
hence a community gaining in its female population through 
migration would tend to have a high G index, while one losing 
through migration would have a low G. 

To summarize the discussion of the AG correlations, it seems 
clear that the relationship between fecundity and the age distribu- 
tion of the women is due, not to differences in the average age of 
women 21 to 44, but to other factors. Correlation between present 



DIFFERENTIAL FECUNDITY IN IOWA 23 

fecundity and fecundity 17 years or so ago is undoubtedly an 
element. Higher fecundity of migrant than non-migrant stock 
may be partly responsible, though my study of interstate migration 
failed to reveal this characteristic among recent migrants (9, p. 50). 
The most probable explanation appears to lie in the high death rate 
among women of the poorer classes; the AG correlation is prob- 
ably a correlation between poverty and fecundity. Analysis of the 
M and P indices later in this study supports this conclusion. 

7. Iowa's Declining Fecundity. For the analysis of causes 
related to the decline in fecundity in Iowa only the G and D indices 
can be readily ascertained for several decades ago. The linear 
regression equation for predicting fecundity of Iowa Counties in 
1915 on the basis of G and D is 

A' DG =612±5— (3.33±.15)(D— 16 ±1) -f(.99±.06) 
(G— 286±3)±37* 
In 1850 in Iowa, G=790, and D was approximately nine (30). 
Substituting these values, the fecundity prediction for Iowa in 
1850 would be 1238±49. The actual fecundity was 1,231. For 
the United States in 1850, G=527; D=20 (approximately) ; and 
the predicted fecundity is 938 ±41. The actual fecundity was 927. 
For whites in the United States in 1910, G=323; D=62.5; and 
the predicted fecundity is 594±39. The actual fecundity is 644. 

The first two of these predictions are within fractions of the 
respective probable errors of estimate; the last exceeds the error 
of estimate by less than one-third. The exact significance of this 
rather surprising accuracy is, however, difficult to determine. In 
1850 the age distribution in Iowa was greatly affected by migration, 
and in those days migrants were more fertile than non-migrants. 
The average age of the women 21 to 44 was less than a year lower 
in 1850 than in 1910. 

14. Home-Ownership and Fecundity. All of the AM correla- 
tions where D is assigned, except one, are negative, and all of the 
AM correlations where D is not assigned, except one, are positive. 
The exceptions are within their probable errors of zero. The fact 
that r A M=+.21±.06 indicates that there is a tendency for counties 
with large percentages of home owners among the middle-aged 
to have high fecundities, but the fact that i' A md= — .37 ±.07 indi- 
cates that, taking urban and rural conditions separately, home 

♦See footnote on page 16. 



24 



IOWA STUDIES IN CHILD WELFARE 



ownership and fecundity are correlated negatively. The inversion 
is due to the fact that more rural than urban persons own homes 
(r DM =— .56 ±.05). 





TABLE 6 






AM Correlations 




r AM 


+.21±.06 


r AM.DINOP - ^° 0' r AM.JLNOP 


+.11±.07 


r AM.D 


— .37±.06 


rAM.D IP -20±.06 


r AML 


+.26±.06 


r AM.DG 


+.04±.07 


r AM.DJNOP •*■« 07 


r AM.LNOP 


+.41±.06 


r AM.DGI 


— .03±.07 


r AM.DOP .13±.07 


r AM.N 


-j-.21±.06 


r AM.DGIQ 


-.14±.07 


r AM.DNOP .12 — .07 


r AM NOP 


+.40 ±.06 


r AM.DGQ 


— .06±.07 


fAM.G +.55±.05 


r AM.O 


-f.26±.06 


r AM.DI 


—.42 ±.06 


r AM .G. +-47±.06 


r A.M.OP 


+.39 ±.06 


r AM.DIJLNOP 


— .17±.07 


Tam.gi +.42±.06 


r AM.P 


+.37±.06 


r AM.DUNOP 


-.19±.07 


'abli +03±.07 
r A M.uLNop --02+.07 


r AM.Q 


+.22±.06 



A clearer idea of the significance of these correlations may be 
gained from the regression equation 

A' DM =612±5— (5.4±.3)(D— 46±1) — (1.0±.2)(M— 143±2)±34. 
In an urban area, where half of the adults 21 to 44 years of age 
owned homes, the predicted fecundity would, according to this 
equation, be — 37 ±72, (or appromimately zero) as compared with 
463 ±45 in an urban area where no such persons owned homes. In 
a rural territory where half of the persons 21 to 44 owned homes 
the predicted fecundity would be 503 ±71 as compared with 
1003 ±45 in a wholly tenant rural community. Even allowing for 
the possibility of curvilinear regression, these predictions indicate 
that, when rural-urban distribution is constant, variations in home 
ownership are associated inversely with very striking differences 
in fecundity. 

It seems probable that the relationship between home ownership 
and fecundity in the city differs from the corresponding relation- 
ship in rural areas. In the country, of course, home ownership is 
practically identical with farm ownership. Linear partial regres- 
sions do not indicate such differences. In order to isolate this 
problem the correlation between A and M was studied in the 31 
counties having less than 40 per cent urban population. In these 
counties r A M= — .67 ±.07, and 

A' M =664±11— (1.7±.2) (M— 157±4) ±30. 
When M is 500, A' M is 81±77; when M is zero, A' M is 931±45. 
Although the very low prediction for areas where all homes are 



DIFFERENTIAL FECUNDITY IN IOWA 



25 



owned is doubtless an exaggeration, due to curvilinear regression, 
there can be little doubt that tenant farmers are reproducing much 
more rapidly than farm owners. If the counties with 40 or more 
per cent urban are considered, r A M=+-33±.07, but this correlation 
is probably due to the DM correlation already pointed out. If the 
11 counties with 70 or more per cent urban are considered 
r AM =-{-.13±.20, which, of course, is practically zero. It seems 
evident, then, that home ownership has little or no relationship 
with fecundity in cities, but a very high relationship with fecundity 
in rural districts. Probably home ownership, apart from the farm 
ownership which it implies in rural districts, is not a good index 
of economic status, since many rather poor workmen in cities buy 
homes on the installment plan, while many very wealthy people 
rent their dwellings. The high fecundity of tenant farmers as 
compared with farm owners has been shown by Kolb (18) and 
Dunlop (8). 

The relationship between delayed marriage and the low fecundity 
of farm owners is not evident. Married persons are more likely 
to own homes than are unmarried persons; ri M =+.39±.06. 

9. Educational Status and Fecundity. Three of the indices 
used in this study relate to educational status, namely, J, 0, and 
L. The J index is the number of males 10 to 18 years of age 
attending school nine months or more, per 1,000 males of these 
ages. The AJ correlations which have been worked out are shown 
in Table 7. 







TABLE 7 










AJ Correlations 






r AJ 


— .52±.05 


r AJ.DMOP 


— .02±.07 


r AJ.LNOP 


— .59±.04 


r AJ.D 


+.07±.07 


r AJ.DNOP 


+.03±.07 


r AJ.M 


— .50±.05 


r AJ.DG 


— .08±.07 


r AJ.DOP 


-f-.Ol— .07 


r AJ.MN 


— .51±.05 


r AJ.DGI 


— .08±.07 


r AJ.G 


— .66±.04 


r AJ.MNOP 


— .48±.05 


r AJ.DGlQ 


— .00±.07 


r AJ.GI 


— .59±.04 


r AJ.MOP 


— .48±.05 


r AJ.DI 


-j-.07±.07 


F AJ.I 


— .42±.06 


r AJ.N 


— .53±.05 


r AJ.DILMNOP 


— .06±.07 


r AJ.ILMNOP 


-.47±.05 


r A J. NOP 


— .59±.05 


r AJ.DIMNOP 


— .07±.07 


r AJ.ILNOP 


— .52±.05 


r AJ.O 


— .54±.05 


r AJ.DINOP 


— .01±.07 


r AJ.IMNOP 


— .47±.05 


r AJ.OP 


— .59±.04 


r AJ.DIOP 


+.01±.07 


r AJ.LMN 


— .54±.05 


r AJ.P 


-58±.05 


r AJ.DLMN 


— .09±.07 


r AJ.LMNOP 


— .48±.05 


r AJ.Q 


— .43±.05 


T AJ.DMNOP 


— .01±.07 


r AJ.LMOP 


— .49±.05 







26 



IOWA STUDIES IN CHILD WELFARE 



For all partials of r A j where D is assigned, the correlations are 
practically 0. For all partials of r A j where D is not assigned, the 
values are — .42 ±.06 or higher. It is clear, therefore, that while 
counties with large percentages of male school attendance for nine 
months or more per year have decidedly fewer children than 
counties with lower standards, the difference is wholly due to the 
fact that urban counties also have the lowest fecundity. 

High School Attendance. Q represents the high school attend- 
ance per 1,000 persons 10 to 17 years of age. The AQ correlations 
are shown in Table 10. 



*AQ 

r AQ.D 

r AQ.DG 

r AQ.DGI 

r AQ.DI 

r AQDIL 



-.42±.06 
.33±.06 
-.17±.07 
-.21±.06 
-.35±.06 
-.26+.06 



TABLE 8 
AQ Correlations 
— .23±.06 

— .21±.06 
+.32±.06 
-.35±.06 
-.45+.07 



'AQ.DIP 
r AQ.DP 



l AQ.G 



-.28±.06 
— .39±.06 
— .43±.06 
-.42±.06 
-.41±.06 
— .38±.06 



Q, although also an educational index, is not influenced by the 
length of the official school year and the efficiency of attendance 
officers, which form considerable elements in J. Q shows rather 
the extent of the desire and ability of parents to secure for their 
children a high school education. One of the factors conditioning 
the sending of children through high school is certainly the 
economic status of the parents. Since therefore both G and Q 
are to some extent economic indices it is to be expected that partial 
correlations of either of them with A when the other is assigned 
will be lower than when the other is not assigned. This proves to 
be the case. In general, it may be concluded that counties where 
high school attendance is low have high fecundity, while where 
high school attendance is high fecundity is low, and that this is 
true when D is assigned. 

Past Education Reported By Adults. L represents the number 
per 1,000 persons over school age who were reported as having 
had eight years or more of schooling. The AL correlations are 
given in Table 9. 

In considering the significance of the above correlations, it must 
be remembered that the L index is built upon the unverified verbal 
statement of a member of the family to a census enumerator, and 



DIFFERENTIAL FECUNDITY IN IOWA 



27 



is doubtless subject to systematic errors due to misstatements and 
exaggerations. It is the opinion of the writer, however, that these 
errors do not vitiate the results; the correlations are consistent 
enough to be accepted at least tentatively. 







TABLE 9 










AL Correlations 






r AL 


— .18±.07 


r AL.DIP 


— .23±.06 


r AL.JMNOP 


— .17±.07 


r AL.D 


— .34±.06 


r AL.DIQ 


— .28±.06 


r AL.M 


— .24±.06 


r ALDG 


— .12±.07 


r AL.DJMNOP 


— .22±.06 


r AL.MN 


— .24±.06 


r AL.DGI 


— .16±.07 


r AL.DJNOP 


— .22±.06 


r AL.MNOP 


— .16±.07 


r AL.DGIQ 


— .11±.07 


r AL.DMNOP 


— .22±.06 


r AL.MOP 


— .15±.07 


r AL.DGQ 


— .08±.07 


r AL.DNOP 


—.22 ±.06 


r AL.N 


— .18±.07 


r ALDI 


— .37±.06 


r AL.DOP 


— .21+.06 


r AL.NOP 


— .12±.07 


r AL.DIJMNOP 


— .21±.06 


r AL.DQ 


— .25±.06 


F AL.O 


— .17±.07 


r AL.DIJNOP 


— .22±.06 


r AL.G 


+.01±.07 


r AL.OP 


-.11±.07 


r AL.DIMNOP 


— .21±.06 


r Ai.I 


-.27±.06 


r AL.P 


-.11±.07 


r AL.DINOP 


— .22±.06 


r AL.IJMNOP 


-.16±.07 


r AL.Q 


— .05±.07 


r AL DIOP 


— .24±.06 


r AL IMNOP 


-.15±.07 







Although J and Q, the indices of present educational status, are 
positively correlated with D, indicating that city children are 
getting better educational advantages than rural, r DL is practically 
zero. This means that rural adults report themselves as having 
had eight years or more of schooling just about as often per 
1,000 as city adults. With L, as in the case of other indices, the 
assignment of G- greatly reduces the correlation with fecundity. 
G and L are negatively correlated (r GL = — .36 ±.06). Both G and 
L appear to be associated with low economic status, one positively, 
the other negatively. 

The significance of the educational indices Q and L is clearer 
when studied in regression form. The regression coefficients of 
fecundity on Q and L respectively when D and I are constant are 
b A Q.DiL= — .20 ±.05 and b A LDi<i= — .32 ±.08. The error of estimate 
in predicting A from D, I, L, and Q is ±31. These coefficients 
mean that, assuming D and I as constant, and neglecting the pos- 
sibility of curvilinear correlation, a community in which all chil- 
dren of high school age attended high school, and in which all 
persons over school age reported having had eight years or more 
of schooling, fecundity rates would tend to be 420±90 lower than 
in a community where no children attended high school and no 
persons over school age reported having had eight, years or more 



28 



IOWA STUDIES IN CHILD WELFARE 



of schooling. Of the 420 difference, 320 would be assignable to 
factors correlated with L, and only 100 to factors correlated with 
Q, in spite of the fact that the partial correlations r AQD i and 
ital di are very nearly equal. The data thus indicate clearly that 
educational status, especially past schooling, is very definitely in- 
versely correlated with fecundity. 





TABLE 10 






AN Correlations 




r AN 


+.01 ±.07 


r AN.DIOP 


+.15±.07 


r AN.JLMOP 


— .03±.07 


r AN.D 


-j-.08±.07 


r AN.DlP 


+.25+.06 


r AN.LMOP 


— .11±.07 


r AN.DG 


— .09±.07 


r AN.DJLMOP 


— .10±.07 


r AN.M 


-f.03±.07 


r AN.DGI 


-f.l3±.07 


r AN.DMOP 


— .08±.07 


r AN.MOP 


— .09±.07 


r AN.DGIQ 


+.11±.07 


r AN.DOP 


— .09±.07 


r AN.O 


— 03±.07 


r AN DGQ 


— .12±.07 


r AN.G 


— .11±.07 


r AN.OP 


— .05±.07 


r AN.DI 


+.24±.06 


r AN.I 


+.34±.06 


r AN.P 


— .03±.07 


r AN.DIJLMNOP~T - l° 07 


r AN.IJLMOP 


+.29±.06 


r AN.Q 


— .06±.07 



10. Religious Indices. Proportion Catholic. The N index, it 
will be remembered, is the percentage of the population reported 
as being members of Catholic churches. The AN correlations 
become appreciably greater than their probable errors only when 
the proportion married is assigned. The correlation r A N.i=+.34 
±.06 is high enough to be definitely significant. The original 
reports as to church membership may, of course, be in error The 
statistics of churches appear to have been somewhat care essly 
handled by the state census office. The total number of church 
members for Winneshiek County is reported as 177,185, although 
the sum of the memberships of the various denominations as given 
for that country is 10,235. Similarly the total given for Wood- 
bury County is 617,087 instead of 20,087, and for Wright County 
is 167,304 instead of 5,954. (13, pp. 733-4). It is possible that 
other errors occurred and were not discovered; if this were true 
it would probably tend to lower the correlations. Prom the data 
as they stand with the above corrections, however, it seems probable 
that the tendency of married Catholics to have more children than 
married non-Catholics is offset by the fact that Catholics in Iowa 
tend to marry later than non-Catholics (ri N = — .51±.05). 

Proportion Protestant. The O index — of non-Catholic church 
members per 1,000 of population — is open to the same doubts as 
to accuracy as is the N index. The highest AO correlation is 



DIFFERENTIAL FECUNDITY IN IOWA 



29 





TABLE 11 






AO Correlations 




r A o 


— .09±.07 


r AO.DIJLMNP .13±.07 


r AO.JLMNP 


-.12±.07 


r AO.D 


— .26±.06 


Tao.dip -.2B±.06 


r AO.LMN 


— .20±.06 


r AO.DG 


+03±.07 


r AO.DJLMNP -14±.07 


r AO.M 


— .18±.07 


r AO.DGI 


-.06±.07 


Tao.dlmn -16±.07 


r AO.MN 


— .18±.07 


r AO.DGIQ 


— .00±.07 


r A0G +.14+.07 


r AO.N 


— .09±.07 


r AO.DGQ 


+09±.O7 


r AO.i -.26+06 


r AO.P 


— .03±.07 


r AO.DI 


— .33±.06 


r AO.IJLMNP —.11 — -07 
r AOJLMN .14±.07 


r AO.Q 


+.05±.07 



rAODi= — .33 ±.06. This negative correlation disappears when G 
is kept constant, and is quite low when M is constant, indicating 
that the tendency for Protestants to have fewer children than non- 
Protestants is due to incidental economic and educational rather 
than religious differences. 





TABLE 12 






AP Correlations 




r AP 


+.21 ±.06 


r AP.DJLMNO 


+.30 ±.06 


r AP.JLMN 


+29±.06 


r AP.D 


-j-.48±.05 


r AP.DLMN 


+.31 ±.06 


r AP.JLMNO 


+.29±.06 


r AP.DG 


-,10±.07 


r AP.DQ 


+.42±.06 


r AP.LMN 


+.33±.06 


r AP.DGI 


+.00±.07 


r AP.G 


— .27±.06 


r AP.M 


+.37±.06 


r AP.DGIQ 


— .03±.07 


r AP.I 


+40±.06 


r AP.MN 


+.37±.06 


r AP.DG<3 


— .13+.07 


r AP.IG 


— .08±.07 


r AP.N 


+.21±.06 


r AP DI 


+.55±.05 


r AP.IJLMNO 


+.38±.06 


r APO 


+.20±.06 


r AP.DIJLMNO 


-j-.38±.06 


r AP.J 


+36±.06 


r AP.Q 


+.12±.07 


r AP.DIQ 


+.50±.05 











11. Fecundity of the Foreign-Bom. A positive correlation ap- 
pears between fecundity and the proportion of foreign-born in 
every case except where the G index of age distribution is assigned. 
r AP =+.21±.06, and r A p.Di=+.55±.05. The difference between 
these two coefficients is due to two factors: first that the slight 
concentration of the foreign-born in cities tends to cover up their 
high fecundity as compared with the native-born, and second that 
the proportion married is negatively correlated with proportion of 
foreign-born (ri P = — .27±.06). This seems at first in contradic- 
tion to the well-known fact that foreign-born women marry earlier 
than native women; see Hart (9, p. 125). The explanation perhaps 
is that where foreign men are in excess native women have fewer 
attractive opportunities to marry. 



30 



IOWA STUDIES IN CHILD WELFARE 



In terms of a partial regression, b A p.Di=+-62±.06, with an error 
of estimate of ±30. The whole theoretical range of P (1,000 
points) therefore corresponds with a difference of 620±61 in fecun- 
dity. If D and I were at their average values, a wholly foreign- 
born community would have a fecundity of 1,166 ±62, while a com- 
munity entirely native-born would have a fecundity of 546±31. 
This contrast is in harmony with what is known of the relative 
fecundity of native and foreign mothers. See (11), (19), (37), 
(38). 

The most striking fact about these AP correlations is that the 
well known tendency of the foreigner to be prolific disappears en- 
tirely if the age distribution index G is assigned (r AP . D Qi=+.00± 
.07). This fact leads to an analysis of the reasons for the high 
correlation r G p=+.70±.04. For the nine counties having the 
largest percentages of foreign-born residents in 1915 the values of 
G were calculated separately for the native-born of native parents, 
native-born of foreign or mixed parentage, foreign-born, and all 
persons of foreign parentage. The results are shown in compari- 
son with similar data for the state as a whole in Table 13. 




As to age distribution the foreign-born of these nine counties 
are clearly not typical of the foreign-born of the state as a whole, 
but tend to have markedly younger women. This youthfulness 
extends also to the other nativity groups in these counties. The 
explanation for these facts seems to be that foreigners tend de- 
cidedly to migrate into communities where economic conditions, as 
reflected by the G index, are unfavorable. Apart from their 
poverty, as shown by this index, the foreign-born appear to be no 
more fertile than the native-born. Differential fecundity as 



DIFFERENTIAL FECUNDITY IN IOWA 



31 



between nativity groups seems from these partial correlations to 
be due to economic and educational differences rather than to 
biological racial differences. 







TABLE 14 








AI Correlations 




r A i 


+.48±.05 


r AI.DOP 


+.41±.06 


r AI.LMN 


+.54±.05 


r AI.D 


+.21±.06 


r AI.DP 


+.37±.06 


r AI.LMNOP 


+.57±.05 


r AI DO 


+.35±.06 


F AI.DPQ 


+.41±.06 


r AJ.LMOP 


+.55±.05 


r AI.DGM 


+.35±.06 


r AI.DQ 


-f.25±.06 


r AI LNOP 


+.64±.04 


r AI.DGQ 


+.37±.06 


r AI.O 


+.57±.05 


r AI.M 


+.44±.05 


r AI.DJLMNOP 


+.44±.05 


r AI.JLMN 


+.54 ±.05 


r AI.MN 


+.53±.05 


r AI.DJLMNO 


+.41±.06 


r AI JLMNO 


+.53±.05 


r AI MNOP 


+.58±.05 


r AI DJLMNP 


4-.47±.05 


r AI.JLMNOP 


+.57±.05 


r AI.MOP 


+.54±.05 


r AI.DJMNOP 


+.45±.06 


r AI JLMNP 


+.58±.05 


r AI.N 


+.56±.05 


r AI.DJNOP 


+.43±.06 


r AI JLMOP 


+.51±.05 


r AI.NOP 


+.64±.04 


r AI DLMN 


+.40±.06 


r AI.JLNOP 


+.58±.05 


r AI.O 


+.52±.05 


r AI DMNOP 


+.44 ±.05 


r AI JMNOP 


+.57±.05 


r AI.P 


+.57±.05 


f AI.DMOP 


+.42±.06 


r A i.j 


+.36±.06 


r AI PQ 


+.56±.05 


r AI DNOP 


+.43±.06 


r AI.L 


+.51±.05 


r AI.Q 


+.50±.05 



12. Fecundity and Proportion Married. The lowest AI corre- 
lation is +.25 ±.06, so that a positive correlation is, as might have 
been expected, clearly established. Fecundity tends to be highest, 
other things being equal, where the largest proportion of the women 
are married. 

13. General Interpretation of Eecundity Correlations. Im- 
portant factors conditioning the fecundity of Iowa women may be 
classified, relative to this investigation, under four heads: 

1. The factors of age distribution, urban or rural residence, 
percentage married, and percentage of persons of high school 
age attending high school. 

2. Factors correlated with the indices listed under 1, such 
as high previous fecundity in the community, high adult 
female death rate, percentage of population foreign-born, 
percentage of middle aged persons owning homes, Catholic 
and Protestant religious affiliations, percentage of male chil- 
dren of school age attending school nine months or more in 
preceding year, and percentage of persons over school age -e- 
ported as having had eight years or more of schooling. 

3. Factors which are independent of the factors listed under 
1, and which vary markedly as between Iowa comities in 1915. 



32 IOWA STUDIES IN CHILD WELFARE 

4. Factors which are independent of the factors listed under 
1, and which vary, or might vary, markedly as between fam- 
ilies, but which do not vary greatly as between Iowa counties 
in 1915. 
Intelligent appraisal of the results of this study depends to a 
considerable extent upon the determination of the relative im- 
portance of these four groups of factors. For this purpose multiple 
regression equations, predicting A in terms of other variables, are 
requisite. The most important of these equations are the 
following :* 

A' DIJLMNOP =612±6-(4.0±.3)(D-46±l) + (.70±.10)(I-746±3) 

— (.03±.04)(J-467±8)-(.13±.04)(I^703±5) 

— (.46±.18)(M— 143±2)+(.11±.05)(N— 80±5) 

— (.09±.04)(O-253±5)+(.42±.07)(P— 106±3)±28. 
A' DG1Q =612±6-(3.5±.2)(D-46±l)+(.74±.05)(G-286±3) 

+(.38±.07)(I— 746±3) — (.18±.06)(Q-164±3)±23. 
A' DG i=(l+.000776 i)(.79g+1.34 D— .048 D=+662)±23. 
A' D0I =( l-j-000775 i)(I+.00241 c) (650+1.7 D-.05D2)±23. 

In the above equations capital letters stand for the original 
items and small letters for deviations from the respective arithmetic 
means of the corresponding indices. The last number in each 
equation is the probable error of estimate. In the last equation 
a new index C is introduced, consisting in 1,000 times the number 
of women 18 to 20 years of age, divided by the number 18 to 20 
plus the number of women over 44 years. If the G index is 
^»o_ e , the C index is i™t e . 

f e + I 

The best summary statement of the accuracy with which these 
various regression equations predict fecundity is the correlation 
of each series of predictions with the corresponding actual fecun- 
dities. These may be calculated readily from the errors of estimate 
of the regression equations and the standard deviation of the 
fecundities. Between the actual fecundities and the predictions 
from the D, I, J, L, M, N, O, and P indices the correlation is 
-4-.86±.02. Between actual fecundities and the predictions from 
the D, G, I, and Q indices the correlation is 4-.91±.01. The 
correlations between actual fecundities and predictions from the 
D, G, and I indices, and between actual fecundities and predictions 



•In these, as in previous, regression equations, the probable errors of the means and 
of the regression coefficients are given after thier respective quantities, with + signs, 
and the probable error of estimate is given at the end of each equation with a ± sign 
before it. 



DIFFERENTIAL FECUNDITY IN IOWA 33 

from the D, C, and I series are also 4-.91±.01. The probable errors 
of these correlations are derived by the usual formula. 

The size of these correlations shows that factors in group 3 on 
page 31 are practically negligible as compared with factors in 
groups 1 and 2. 

14. Curvilinear Regressions. Thus far the discussion has been 
confined to rectilinear regressions. The assumption of rectilinearity 
is, however, misleading. If, in the equation for predicting A from 
D, I, J, L, M, N, 0, and P the theoretical extremes of all the 
variables, corresponding with the lowest fecundity, are substituted, 
the predicted fecundity becomes —466 ±108, which is of course 
absurd. Such a prediction should represent the fecundity of a 
community consisting entirely of unmarried, well educated, home 
owning, native-born city folks. Such a community would have a 
certain illegitimate birth rate, and a certain number of adopted 
children. At the least it could not have less than no children at 
all. In the equation for predicting A from D, G, I, and Q, while 
G, strictly speaking has no definite limit, the same possibility of 
a prediction of negative fecundity appears. 

The reason for this irrationality at the lower extremes seems 
to be that the effect of the proportion married on fecundity is to 
be measured by multiplication rather than addition. If the pro- 
portion married became zero the fecundity would drop approxi- 
mately to the illegitimate rate, and the absolute effects of other 
conditions would be very much smaller than in a population where 
all women of marriageable age were married. The equations pre- 
dicting A from D, G, and I, and from D, C, and I, attempt to 
meet this difficulty. It will be noted that, in spite of not using 
the variable Q, these equations fit the data slightly better than the 
second equation. Moreover, assuming G and C as being 0, the 
lowest possible predictions from the equations involving curved 
regression surfaces are 40 and 63 respectively, as compared with 
— 127 as the lowest restilinear prediction from D, G, I, and Q. 

The prediction of A from R, I, J, L, M, N, O, and P fits much 
less closely than the other three equations do. The age index more 
than takes the place of the religious, economic, educational and 
nativity indices. The implication seems to be that the age index 
better reflects the economic and intellectual factors associated with 
fecundity than do the omitted indices. 



34 IOWA STUDIES IN CHILD WELFARE 

While the above argument proves the unimportance of factors 
which vary markedly from county to county and which are un- 
correlated with D, G, and I, it is not possible to prove that the 
correlation of A with D, 6, and I may not be due to some variable 
not measured in the investigation. The factors still remain which, 
while they vary little as between Iowa counties, do or might vary 
greatly as between individual families. Sterility due to venereal 
disease may, perhaps, be a case in point, though, on the other hand, 
venereal disease may be correlated with the percentage of urban 
population, the percentage married, and the age distribution, and 
so fall into one of the preceding groups of factors. Innumerable 
other factors, however, such as presence or absence of essential 
elements in diet, prevalence of sterilization by surgical means, 
the introduction of new methods of contraception, fluctuations in 
fashion, the onset and duration of war, biological changes due to 
selective factors now at work, and so on indefinitely, while they 
have been proven to exert little independent differential influence 
on fecundity as between Iowa Counties in 1915, might conceivably 
have profound influence upon fecundity within counties in 1915, 
or in Iowa at some other date, or in other localities at any time. 

IV. CONCLUSIONS 

14. Summary. The fecundity of Iowa Counties may be pre- 
dicted best from three variables among those experimented with : 
namely, percentage of urban population (D), proportion of women 
married (I), and steepness of adult female age distribution (G). 
These three factors, and conditions correlated with them, are 
responsible for at least five-sixths of the variation between Iowa 
counties in the number of children under five years per 1,000 
women 21 to 44 years of age. City people have fewer children 
than rural, and unmarried women, of course, have fewer than 
married women. The significance of the G index seems to be as 
follows: Poor people have high birth rates and high adult female 
death-rates. The poor in one generation tend to be the parents of 
the poor in the next generation. There thus arises a more or less 
continuous poor class with a steep female age distribution, reflected 
in a high G index. In rural districts poor people are tenant 
farmers; hence their M index is low. Poor people leave school 
early, hence their Q and L indices are low. Foreign-born immi- 



DIFFERENTIAL FECUNDITY IN IOWA 35 

grants arc mostly poor, and migrate into poor districts; hence 
indices 6 and P are associated. The fact that the correlations of 
A with L, M, N, 0, P, and Q are practically negligible when G is 
constant means that all of these correlations express the same fact, 
namely that the poor and ill-educated — the unsuccessful in a word 
— are the highly fecund class. Doubtless also the rearing of chil- 
dren interferes with economic success. G appears to be the best 
available summary index of this condition. It might well be called 
the index of misery. 

The types of individual, then, who are becoming parents most 
extensively in Iowa are the tenant farmer, the foreigner, and the 
badly educated. The types most meagerly participating in the 
bearing and rearing of the next generation are the economically 
successful, the native-born, the highly educated, and the city 
dwellers. These differences in fecundity are so radical that they 
cannot fail to have a profound effect upon the types of children 
produced, upon the sort of home and community environments pro- 
vided for them, and hence upon the trend of character of the 
Iowa population. 

Such conditions, in cooperation with the selective emigration to 
cities and to other states pointed out in a previous study (9) tend 
strongly to aggravate the danger of progressive social deterioration 
in the least favored rural areas of the State. 

15. Recommendations. The first need in connection with this 
problem is for further research. The counties of the state furnish 
only 99 itrnis — a number too small for really satisfactory analysis 
by means of partial correlations. The counties are, moreover, too 
large, and vary too little among themselves in many respects, to 
permit safe generalizations about regressions at the extremes of 
the theoretical ranges, or to determine the nature of the curvilinear 
regressions which undoubtedly represent the true relationships. 

Research along the following lines is called for: 

1. Securing of accurate detailed social data for several thousand 
individual families, with a view to analysis of their fecundities 
in correlation with factors indicative of their success and desir- 
ability as parents, and as to the conditions which encourage and 
discourage fecundity. 

2. Analysis of these data with a view to expressing fecundity 



36 IOWA STUDIES IN CHILD WELFARE 

in terms of a curvilinear regression equation with an error of 
estimate approaching zero. 

3. Formulation of definite social policies on the basis of these 
findings, with a view to counteracting the menace of progressive 
deterioration in the quality of parenthood. 



V. SELECTED REFERENCES 

INVOLVING STATISTICAL DISCUSSION OF DIFFERENTIAL 

FECUNDITY 

1. Brend, William A., Passing of the Child. Nineteenth Century, 
V 1915, (77), 584-602. 

2. Cattell, J. McKeen, Families of American Men of Science. Amer- 

ican Men of Science. Garrison, N. Y.: The Science Press, 1921 
781-808. 

3. Crum, Frederick S, The Decadence of the Native American Stock. 

A statistical Study of Gencological Records. Pijb. Am. Stat. 
Assn. (14) 215-22. 

4. Darwin, Leonard, Eugenics in Relation to Economics and Statis- 

tics. Jrnl, Royal Stat. Soc, 1919, (82), 1-27. See also 1916, 
(76), 159-175. 

5. Dublin, Louis I. Mortality Statistics of Insured Wage-Earners 

and Their Families. New York: Metropolitan Life Insurance 
Co. 1919. 

6. Dublin, Louis I. Significance of the Declining Birth Rate. 

Science, 1918. (470) 201-210. 

7. Dudfield, Reginald. Some Unconsidered Factors Affecting the 

Birth-Rate, with Bibleography. Jrnl. Royal Stat. Soc. 1908, 
(71) 1-55. 

8. Dunlop, James Craufurd. The Fertility of Marriage in Scotland: 

A Census Study. Jrnl. Royal Stat. Soc. 1914, (77), 260-288, 
313-316, 1915, (78), 35-45. 

9. Hart, Hornell. Selective Migration, University of Iowa Studies 

in Child Welfare, 1921. 

10. Heron, David. On the Relation of Fertility in Man to Social 

Statiis and on the Changes in this Relation That Have Taken 
Place in the Last 50 Years. Draper's Company Research 
Memoirs. London : Dulau & Co. 1906. 

11. Hill, Joseph A. Comparative Fecundity of Native and Foreign 

Parentage in the United States. Pub. Am. Stat. Assn. 1913, 
(13) 583-604. "Abridged from the Report of the U. S. Immi- 
gration Commission, Vol. 28." 

12. Hoffman, Frederic L. The Decline in the Birth-Rate. North Am. 

Rev. May, 1909. (189) 675-687. 

13. Iowa State Census for 1915. Des Moines: Robert Henderson, 

State Printer. 

14. Johnson, Stewart. The Relation Between Large Families, Poverty, 

Irregular Earnings, and Crowding. Jrnl. Royal Stat. Soc, 1912. 
(75), 539-550. 

37 



38 IOWA STUDIES IN CHILD WELFARE 

15. Kelley, Truman. Chart to Facilitate the Calculation of Partial 

Coefficients of Con-elation and Regression Equations. Stan- 
ford University Publications. 1921. 

16. Kelley Truman. Tables: To Facilitaoe the Calculation of Partial 

Coefficients of Correlation and Regression Equations. Austin, 
Texas: University of Texas. 191G. 

17. Knibbs, G. H. The Mathematical Theory of Population., of its 

Character and Fluctuations, and of the Factors which Influence 
Them. Census of the Commonwealth of Australia for 1911. 
Melbourne: McCanon Bird & Co. 1911. Appendix A, Vol. V. 

18. Kolb, J. H. Rural Primary Groups. Madison: University of 

Wisconsin Research Bulletin 51, 1921, 42. 

19. Kuczynski, R. P. The Fecundity of the Native and Foreign-Born 

Population in Massachusetts. Quart. J ml. Econ. 1901 and 1902 
(16) 1-36; 141-186. 

20. National Birth-Rate Commission. The Declining Birth-Rate, Its 

Causes and Effects. London: Chapman & Hall. 1916. 

21. National Birth-Rate Commission. Problems of Population and 

Parenthood. New York: E. P. Dutton & Co. 1920. 

22. Nearing, Nellie Seeds. Education and Fecundity. Pubs. Am. Stat. 

Assn. 1914, (14) 156-174. 

23. Newsholme, A., and Stevenson, J. H. C. Human Fertility in the 

United Kingdom and Other Countries, as Shown by Corrected 
Birth-Rates. Jrnl Royal Stat. Soc, 1905, (49) 34ff. 

24. Pearson, Karl, Chances of Death. London, New York: E. Arnold, 

1897. Vol. I, 63-102. 

25. Pearson, Karl. Effect of a Differential Fertility on Degeneracy. 

Biometrika, 1909-10, (7) 258-275. 

26. Powys, A. O. Data for the Problem of Evolution in Man on Fer- 

tility, Duration of Life, and Reproductive Selection. Biometrika, 
1905-6. (4) ,233-285. 

27. Smith, Mary Roberts. Statistics of College and Non-College 

Women. Pubs. Am. Stat. Assn. 1900 (7) 1-26. 
£8. Thorndike, Edward L. The Decrease in the Size of American 
Families. Pop. Sci. Mo. 1903 (63) 64-70. 

29. U. S. Census. 1910, I. 

30. U. S. Census. 1850, pp. xlii, ff. 

31. U. S. Census— Birth Statistics 1910. Washington: Govt. Print. 

Office, 1921. 

32. U. S. Department of Labor, Children's Bureau. Infant Mortality 

Series. Washington: Govt. Print, Off. 1915 seq. 

33. U. S. Department, of Labor, Children's Bureau. Maternal Mor- 

tality. Washington: Govt. Print. Off. 1917. 52.. 
". ! . Webb, Augustus D. New Dictionary oi Statistics.^ London : Geo. 

Rontledge & Sons, 1911. 67. 
35. Welton, Thos. A. On the Birth-Rates in Various Parts of England 

and Wales. Jrnl. Royal Stat. Soc, 1916, (79), 18-36; 1917, (80). 



DIFFERENTIA], FECUNDITY IN IOWA 39 

36. Whipple, George Chandler. Vital Statistics. New York: John 

Wiley & Sons. 1919. 196ff. 

37. Wilcox, W. F. Proportion of Children in the United States. 

Washington: Govt. Print. Off. 1905. , 

38. Young, Allyn A. The Birth-Rate in New Hampshire. Pub. Am. 

Stat. Assn. 1905 (9) 263-281. 

39. Yule, G. Udney. Changes in the Marriage and Birth-Rates in 

England and Wales During the Past Half Century, with an 
Inquiry as to the Probable Causes. Jml. Roya! Stat. Soc, 1905 
(49) 88ff. 

40. Yule, G. Udney. The Fall of the Birth-Rate. Cambridge Univ. 

Press. 1920. 

41. Yule, G. Udney. Introduction to the Theory of Statistics. Lon- 

don: Charles Griffin & Co. 1919. 



LIBRARY OF CONGRESS 




Obtainable from the University 
Librarian ; Price $0.80 



